Evidence map›Paper›PMID 42000761›Full record

ArticleScientific reports2026

Enhancing long-term comprehensive operation of cascade hydropower system using an improved multi-objective RIME optimization.

Aolin Gao, Hu Hu, He Li, Lyuwen Su, Zhe Yang, Kaixu Geng, Cihang Shan

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Aolin GaoSchool of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou, China.
Hu HuSchool of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou, China. huhu@zzu.edu.cn.
He LiSchool of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou, China.
Lyuwen SuJiangsu Hydraulic Research Institute, Nanjing, China.
Zhe YangCollege of Water Resources and Architectural Engineering, Northwest A&F University, Yangling, China.
Kaixu GengSchool of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou, China.
Cihang ShanSchool of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou, China.

Funding

Natural Science Foundation of Henan Province 242300420309
6 · The paper itself

Abstract

For cascade hydropower system (CHS), multi-objective long-term comprehensive operation (MOLTCO) is important for balancing the conflicting objectives of hydropower production and ecological sustainability. This paper proposes an Improved Multi-objective RIME algorithm (IMORIME) to address the MOLTCO problem, aiming to simultaneously maximize power generation and minimize ecological flow deviations. The presented IMORIME introduces Good Point Set method for uniform population initialization and incorporates a hybrid constraint handling strategy combining boundary clipping and penalty functions to effectively ensure solution feasibility. A crowding-weighted roulette selection strategy is adopted to determine guided solutions, adaptively balancing convergence and diversity. Moreover, IMORIME introduces an improved hard rime puncture mechanism inspired by Differential Evolution to mitigate stagnation at local optima and to maintain Pareto front diversity; a Whale Optimization Algorithm spiral enhancement strategy to reinforce local exploitation and accelerate convergence; and a Quasi-Opposition-Based Learning strategy to broaden the search space and preserve global diversity. The efficacy of the proposed algorithm is verified through benchmark tests and practical applications in the Three Gorges and Gezhouba cascades. Quantitative comparisons against five classical algorithms reveal that IMORIME consistently achieves the top average rank (1.0) in both Hypervolume and objective function values across all inflow scenarios. Specifically, in the highly constrained low-flow scenario, IMORIME reduces the ecological flow deviation by 10.5% and improves the Hypervolume metric by 3.1% compared to the second-best algorithm. Furthermore, IMORIME consistently maximizes total hydropower generation while maintaining the most well-distributed Pareto front (yielding the optimal average Spacing rank of 1.3). In the practical cascade system application, IMORIME demonstrates superior convergence stability and robustness, effectively balancing generation benefits and ecological protection in MOLTCO for CHS with coupled generation–ecology objectives.

Indexed as

Cascade hydropower systemEcological operationLong-term comprehensive operationMulti-objective RIME algorithm

Identifiers

PMID42000761
PMCPMC13250133

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.